Urban ecological land and natural-anthropogenic environment interactively drive surface urban heat island: An urban agglomeration-level study in China
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Bibliographic record
Abstract
The surface urban heat island effect (SUHI) that occurs during rapid urbanization increases the health risks associated with high temperatures. Urban ecological land (UEL) has been shown to play an important role in improving urban heat stress, however, the impact of UEL interactions with the natural-anthropogenic environment on SUHI at the urban agglomeration-scale is less explored. In this study, the Google Earth Engine and GeoDetector were applied to characterize the spatiotemporal patterns of UEL and SUHI in the Guangdong-Hong Kong-Macao Greater Bay Area from 2000 to 2020 by extracting major built-up urban areas and quantifying the impacts of UEL and its interactions with the natural-anthropogenic factors on SUHI. The results show that the evolution of the UEL landscape structure exhibits clear spatiotemporal coupling with SUHI. Specifically, the UEL underwent a dispersion and degradation process in 2000-2015 and a convergence and restoration process in 2015-2020, the SUHI correspondingly transitioned from intensification and continuity to mitigation and contraction. The UEL landscape structure showed a notable impact on the SUHI reduction, and the dominance and richness of the patches explained an average of 19.95% and 16.03% of the SUHI, respectively. Moreover, the interaction between UEL and land urbanization rate and anthropogenic heat release had a dominant effect on SUHI, but this effect significantly declined from 2015 to 2020. With the implementation of ecological restoration projects, the interaction of UEL with topography rapidly increased and the SUHI gradually dominated by the joint interaction of UEL and natural-anthropogenic factors. A synthesis of the varying effects of several factors showed that the dynamic relationship between the development stages of the urban agglomeration's regional system and SUHI may conform to the Environmental Kuznets Curve. SUHI reduction strategies should therefore comprehensively optimize the rational allocation of UEL landscape structures and natural-human elements to promote the well-being of residents.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it